A research team has developed a neural network-based framework that significantly improves the prediction of satellite clock bias (SCB) for low earth orbit (LEO) satellites, addressing a major challenge for next-generation navigation systems. 

Glomerella leaf spot (GLS), caused by the fungus Colletotrichum gloeosporioides, is a major threat to apple production worldwide, causing severe defoliation and fruit necrosis.

With the increasingly urgent demand for non-toxic, high-specific-impulse propellants in space propulsion systems, ammonium dinitramide (ADN)-based liquid propellants have attracted extensive attention as a new-generation green alternative to hydrazine-based fuels. However, existing ADN-based thrusters all adopt the catalytic ignition technology route, which suffers from inherent drawbacks: the catalyst cannot withstand temperatures exceeding 1500 K, long preheating times are required prior to startup (in the Prisma mission, in-flight preheating lasted 600–720 s with a single-event energy consumption of 25 kJ), and insufficient preheating may lead to "hard start" or even explosion—these issues severely constrain the rapid response capability and operational safety of the thrusters. Although active ignition methods such as resistive ignition and laser ignition have been validated at the single-droplet level, how to achieve catalyst-free electric ignition at the thruster system level and systematically evaluate its combustion characteristics remains a critical engineering problem urgently to be addressed in the field of green space propulsion.

Plasma bubbles in the ionosphere over equatorial and low-latitude regions are a common nighttime plasma density depletion structure that can cause severe scintillation interference on high-frequency communications, making them an important target of space weather monitoring. Traditional detection methods rely on statistical thresholds derived from temporal fluctuations in ion density; however, plasma bubbles exhibit significant variability in spatiotemporal scales (ranging from kilometers to hundreds of kilometers), and their occurrence frequency is strongly modulated by solar activity and seasonal factors. Consequently, fixed-threshold methods struggle to accommodate this diversity, with miss rates reaching up to 40% during periods of low solar activity. Although deep learning has demonstrated powerful capabilities in automatically extracting and recognizing complex waveform features in fields such as medical image segmentation and signal peak detection, its systematic application to ionospheric plasma bubble detection—and thus addressing the adaptability deficiencies of traditional threshold methods—remains underexplored.

With the long-term on-orbit operation of space stations, extravehicular status monitoring and surface inspection tasks have imposed higher demands on the motion planning capabilities of manipulators. Space redundant manipulators, when performing visual monitoring tasks, must simultaneously satisfy multiple complex constraints including kinematics, dynamics, obstacle avoidance, and end-effector line-of-sight, making this a typical nonlinear non-convex optimal control problem. Although sampling-based methods can rapidly generate collision-free paths, they struggle to guarantee dynamic feasibility in high-dimensional systems and often yield poor-quality paths. Optimization-based methods, while capable of directly generating high-quality trajectories, are highly sensitive to initial solutions and lack effective warm-start mechanisms. Therefore, how to merge the rapid search capability of sampling-based methods with the precise solving advantage of optimization-based methods to plan high-quality executable trajectories under complex constraints for space manipulators has become a key challenge in advancing the intelligent development of space robotics.

Reducing excessive boundary-layer mixing helps ocean models recover internal lee waves

The Journal of Neurorestoratology (JNR) announces a significant milestone in its development: the journal officially returns to SciOpen, a world-class independent digital publishing platform developed by Tsinghua University Press. This transition marks a new chapter for JNR and embraces a more autonomous, globally connected publishing future.

A study of Scots pine plantations across Mediterranean Spain, temperate Romania and Northeast China found that climate constraints on growth vary by region. Soil moisture was the main limitation in dry Spanish sites, while temperature played a larger role in Romania and Northeast China. Spanish plantations showed sharper drought-related growth declines but stronger average recovery, although many did not recover within four years. The findings support region-specific forest management and improved models incorporating long-term drought, regional climate dynamics and local adaptation.

Growing demand for clean energy conversion has increased interest in protonic ceramic fuel cells, but sluggish proton-coupled oxygen reduction at the cathode remains a major barrier. A team led by Jie Hou at the University of South China addressed this challenge by coordinating d-band center regulation with Fe 3d-O 2p orbital alignment in K₂NiF₄-type La_0.5Sr_1.5FeO_4+δ. This strategy promotes oxygen-defect formation, proton incorporation and transport, and ORR kinetics. The optimized cathode achieved 2.2 W cm⁻² and 0.04 Ω cm² at 700 °C and operated stably for over 400 h, offering a promising route for advanced PCFC cathode design.

Researchers have shown that nonaqueous solvent molecules can serve as active interfacial ligands rather than passive transport media. At a layered cyano-bridged metal framework, outer-layer ligands accelerate lithium-ion solvation renewal, while interlayer ligands stabilize a low-curvature surface that strengthens anion anchoring. The cooperative interface raises lithium-ion conductivity and selectivity, and a molecular descriptor based on ligand polarity symmetry provides a route toward predictive solvent selection.